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whoke • today at 6:45 PM • 3 replies • view on HN

"you haven’t done any of the hard thinking you would normally do in writing the code yourself"

It's true, I spend less time solving problems that arise naturally from the process of implementation. But implementation errors have a poor signal-to-noise ratio. For every error that exposes a real design problem there are 10 others that involve routine fixes: type errors, scope issues, import resolution, dependencies. There's a common argument I see being thrown around, which is "how will junior engineers develop into senior engineers if they don't get reps in with implementation?" But to me it feels like "programmer" and "architect" are becoming more and more orthogonal as the models improve in capability. If my goal is to be an architect, getting more reps in as an architect is in my best interest, and letting the agents do the dirty work frees up space for me to spend the mental effort where it counts. And anecdotally, the scope of projects I've started taking on has scaled roughly linearly relative to how much work I'm able to delegate, while the mental engagement has stayed constant.

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michaelchisari • today at 7:18 PM

| type errors, scope issues, import resolution, dependencies.

I write code myself and use the LLM to find mistakes then fix them manually. I recommend inverting the conventional wisdom on LLMs:

  1. Don't use it to write code. It's a terrible
     programmer. But it's an intelligent rubber duck
     and a solid analysis tool.

  2. Write the code yourself. It'll go faster than
     figuring it out as you go along. It's just typing
     if you have a plan.

  3. Use an LLM to sanity check what you wrote.
     It can find potential design issues or future
     problems or breaks from convention. Decide based on
     your experience how much you want to address these
     issues before moving on.
Is this as fast as AI writing the code? Not at first but possibly over time. Maybe faster. Certainly better quality.

Plus you don't lose any of your skills in the process.

You're still doing the gruntwork necessary to keep your skillset.

And the token cost is a small fraction of what it takes to get LLMs to write good code.

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ManuelKiessling • today at 7:28 PM

This is half question, half thought experiment: What exactly is it that makes us think „great, I can delegate coding to the agent, and focus on architecture myself“, instead of „great, I can delegate coding to the agent, and I can delegate architecture to the agent, and I can focus on <third thing> myself“?

And how many levels up can you construct this sentence before things go south?

What I‘m implying with this question is, of course: What makes us so confident that these things are not also better architects than us?

After all there is a huge corpus on how to architect applications correctly, and that’s in the training data just as much as Python best practices and C++ style recommendations.

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dominotw • today at 7:35 PM

ironically llm prove the its not possible to just think "abstract thoughts" . we cannot remove concrete "implmenation" facts like paris is capital of france and retain just abstract "countries have capitals"